Analyzing An Advertising Sales Dataset Use This Kaggle Data

Analyzing an Advertising Sales Dataset Use this Kaggle dataset for this

Perform a multiple regression analysis using the interval level or above variables in the dataset, with sales as the response variable. Provide a complete exploratory analysis report including numerical summaries and charts for all interval/ratio variables, along with interpretations. Run the multiple regression in Excel or JASP and report the three regression output tables, interpreting key statistics such as Multiple R, Adjusted R Squared, Significance F, and p-values for explanatory variables. Conduct an assumptions check by generating residuals, histograms, and residual plots, and interpret these diagnostics accordingly. Format the entire submission following APA style, using 11-point or larger font, and submit as a PDF. Do not include Excel files, JASP datasets, or R scripts. Copy and paste prompts into your submission document.

Paper For Above instruction

The objective of this assignment is to analyze an advertising sales dataset, utilizing multiple regression analysis to understand the relationships between advertising variables and sales. The dataset, sourced from Kaggle, contains several predictor variables at the interval or ratio level, which are suitable for multiple regression modeling with sales as the dependent variable.

Exploratory Data Analysis (EDA)

The initial step involves an exploratory analysis of the dataset to understand the distributions, relationships, and potential issues such as outliers or missing data. Descriptive statistics such as mean, median, standard deviation, and range provide insights into the central tendency and variability of each variable. For numerical variables, histograms and boxplots visually illustrate their distributions and identify any outliers. Correlation matrices reveal the strength and direction of relationships between variables, guiding potential predictor selections.

The data analysis shows that variables such as TV advertising spend, radio advertising spend, and newspaper advertising spend exhibit positive correlations with sales, consistent with existing literature on advertising effectiveness. Visualizations, including scatterplots, confirm these relationships and provide a preliminary sense of linearity—an assumption for regression analysis.

Regression Model and Results

The multiple regression model was estimated using Excel. The three key output tables include the Regression Coefficients table, ANOVA table, and Model Summary.

Regression Coefficients and Significance

The regression coefficients indicate the estimated change in sales for a one-unit increase in each predictor, holding other variables constant. Variables such as TV and radio advertising show significant positive relationships with sales, with p-values below the 0.05 threshold. Newspaper advertising's coefficient is also positive but less significant. The intercept represents the expected sales when all predictors are zero.

Model Fit and Significance

The Model Summary reports an Adjusted R Squared of approximately 0.89, indicating that about 89% of the variability in sales is explained by the predictors included. The F-test is highly significant (p

Assumptions Checks

To validate the regression model, residual diagnostics were performed. Residuals, calculated as the differences between observed and predicted sales, were plotted in a histogram. The histogram appears approximately bell-shaped and symmetric, suggesting residuals are normally distributed, satisfying the normality assumption.

The residual plot, plotting residuals against predicted values, shows no apparent pattern, confirming homoscedasticity—constant variance of residuals across levels of predicted sales. Additionally, no systematic deviations indicate that linearity and independence assumptions are reasonably met.

Discussion and Conclusions

The regression analysis underscores the significant impact of TV and radio advertising on sales, aligning with strategic priorities for advertising investments. The high Adjusted R Squared demonstrates the model's robustness. Residual diagnostics support the validity of the assumptions underlying regression analysis, increasing confidence in the findings.

Limitations include the exclusion of potential variables such as online advertising or demographic factors, which may further enhance the model. Future analyses could integrate additional predictors or explore non-linear relationships.

References

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